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Im planning to work on big data for business intelligence.
Is there any models have been developed before and has been implemented to work with poor date(process the data as it is no preprocessing has been done)
what are the available data sets that is used in business and what are the models has been used in big data in business intellegence

Hi,
This question is so general because business intelligence can mean many things. In general all kinds of data mining technologies could be used such as pattern mining, clustering, classification, regression, sequence prediction etc. Actually, choosing a technique should depend on what is your goal.
For datasets you can check Kaggle it has many datasets and maybe some for what you need. If you want datasets of customer transactions some of them are on the Spmf website.

Thanx for your reply
The datasets u mentioned in kaggle can I work for it for poor quality I mean the date As there is no need for preprocessing so I can consider it as it comes from the sources
For the teqniques I know they are many techniques but my question is the techniques available like svm decision tree .... are suitable for big data or is there another technique .
For business intelligence I mean in the field of business like customers could u please suggest any case study to work with big data

There are many techniques suitable for big data. There is a conference every year called IEEE Big Data for example with hundreds of papers about applying data mining to big data. Besides, there are other conferences and journal with many papers about data mining in big data. Making a list of all the models that can be applied to big data would be too long. I think in general many data mining techniques can be extended to big data.

Case study.. It is also a quite broad question. I work on itemset and association rules. The goal is to find patterns such as what the consumers often buy together in transactions or yield a lot of money (high utility itemset mining). This is an example of case study that is interesting for businesses. There are many others.